In-depth understanding of LSTM and its recent advances in lung disease diagnosis

descriptionPublicationkeyboard_double_arrow_right Article 30 Jun 2022Publisher:GSC Online PressJournal:World Journal of Advanced Research and Reviews, volume 14, pages 517-522 (eissn: 2581-9615,

Copyright policy )

Authors: Abhinandan Kalita;

doi: 10.30574/wjarr.2022.14.3.0602 , 10.5281/zenodo.7731978 , 10.5281/zenodo.7731979

In-depth understanding of LSTM and its recent advances in lung disease diagnosis

- Summary
- Subjects
- Metrics

Abstract

Of late, long short-term memory (LSTM) has proven its worth in medical diagnosis. Hence, there is a need to explore this special version of recurrent neural network (RNN), which can learn long-term dependencies. LSTM addresses the short-term memory problem of basic RNNs. In this paper, an in-depth study of LSTM is done with the help of a few real-life examples. Some of the recent advances of LSTM in COVID-19 and other lung disease diagnoses have also been discussed.

Related Organizations

Girijananda Chowdhury Institute of Management and Technology
India

Keywords

Recurrent neural network; Vanishing gradient; Long short-term memory; Deep learning; COVID-19

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average